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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 14 Dec 2010 14:48:39 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t1292337994dlfkzx8e56aw4kt.htm/, Retrieved Thu, 02 May 2024 23:34:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109704, Retrieved Thu, 02 May 2024 23:34:07 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

125

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
F   PD    [Recursive Partitioning (Regression Trees)] [] [2010-12-14 14:48:39] [6b31f806e9ccc1f74a26091056f791cb] [Current]
-   P       [Recursive Partitioning (Regression Trees)] [] [2010-12-14 15:13:52] [de55ccbf69577500a5f46ed42a101114] 
F   P         [Recursive Partitioning (Regression Trees)] [] [2010-12-14 17:44:43] [de55ccbf69577500a5f46ed42a101114] 
F   P       [Recursive Partitioning (Regression Trees)] [] [2010-12-14 15:17:47] [de55ccbf69577500a5f46ed42a101114] 
-   PD      [Recursive Partitioning (Regression Trees)] [] [2010-12-21 19:59:56] [de55ccbf69577500a5f46ed42a101114] 
- RMPD        [Recursive Partitioning (Regression Trees)] [] [2010-12-22 14:01:34] [de55ccbf69577500a5f46ed42a101114] 
-   PD        [Recursive Partitioning (Regression Trees)] [] [2010-12-22 16:47:45] [de55ccbf69577500a5f46ed42a101114] 
-               [Recursive Partitioning (Regression Trees)] [] [2010-12-22 16:51:58] [de55ccbf69577500a5f46ed42a101114] 
-   PD            [Recursive Partitioning (Regression Trees)] [] [2010-12-25 12:54:19] [de55ccbf69577500a5f46ed42a101114] 
-   PD            [Recursive Partitioning (Regression Trees)] [] [2010-12-25 12:58:37] [de55ccbf69577500a5f46ed42a101114] 

Feedback Forum

2010-12-19 08:38:29 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De door de student opgestelde boomstructuur alsook de interpretatie ervan is correct. Men kan inderdaad zien dat 'horsepower', 'weight' en in minder mate 'engine displacement' de belangrijkste variabelen zijn.

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Dataseries X:

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Histogram

Boxplots

8	350	165	3693	11.5
8	318	150	3436	11
8	302	140	3449	10.5
8	429	198	4341	10
8	440	215	4312	8.5
8	455	225	4425	10
8	383	170	3563	10
8	340	160	3609	8
8	455	225	3086	10
4	113	95	2372	15
6	199	97	2774	15.5
4	97	46	1835	20.5
4	110	87	2672	17.5
4	104	95	2375	17.5
4	121	113	2234	12.5
8	360	215	4615	14
8	307	200	4376	15
8	304	193	4732	18.5
4	97	88	2130	14.5
4	113	95	2228	14
6	250	100	3329	15.5
6	232	100	3288	15.5
8	350	165	4209	12
8	318	150	4096	13
8	400	170	4746	12
8	400	175	5140	12
4	140	72	2408	19
6	250	100	3282	15
4	122	86	2220	14
4	116	90	2123	14
4	88	76	2065	14.5
4	71	65	1773	19
4	97	60	1834	19
4	91	70	1955	20.5
4	97.5	80	2126	17
4	122	86	2226	16.5
8	350	165	4274	12
8	318	150	4135	13.5
8	351	153	4129	13
8	429	208	4633	11
8	350	155	4502	13.5
8	400	190	4422	12.5
3	70	97	2330	13.5
8	307	130	4098	14
8	302	140	4294	16
4	121	112	2933	14.5
4	121	76	2511	18
4	122	86	2395	16
4	120	97	2506	14.5
4	98	80	2164	15
8	350	175	4100	13
8	304	150	3672	11.5
8	302	137	4042	14.5
8	318	150	3777	12.5
8	400	150	4464	12
8	351	158	4363	13
8	440	215	4735	11
8	455	225	4951	11
6	225	105	3121	16.5
6	250	100	3278	18
6	250	88	3021	16.5
6	198	95	2904	16
8	400	150	4997	14
8	350	180	4499	12.5
6	232	100	2789	15
4	140	72	2401	19.5
4	108	94	2379	16.5
4	122	85	2310	18.5
6	155	107	2472	14
8	350	145	4082	13
8	400	230	4278	9.5
4	116	75	2158	15.5
4	114	91	2582	14
8	318	150	3399	11
4	121	110	2660	14
8	350	180	3664	11
6	198	95	3102	16.5
6	232	100	2901	16
4	122	80	2451	16.5
4	71	65	1836	21
6	250	100	3781	17
6	258	110	3632	18
8	302	140	4141	14
8	350	150	4699	14.5
8	302	140	4638	16
8	304	150	4257	15.5
4	79	67	1963	15.5
4	97	78	2300	14.5
4	83	61	2003	19
4	90	75	2125	14.5
4	116	75	2246	14
4	120	97	2489	15
4	79	67	2000	16
6	225	95	3264	16
6	250	72	3158	19.5
8	400	170	4668	11.5
8	350	145	4440	14
8	351	148	4657	13.5
6	231	110	3907	21
6	258	110	3730	19
6	225	95	3785	19
8	262	110	3221	13.5
8	302	129	3169	12
4	140	83	2639	17
6	232	100	2914	16
4	134	96	2702	13.5
4	90	71	2223	16.5
6	171	97	2984	14.5
4	115	95	2694	15
4	120	88	2957	17
4	121	115	2671	13.5
4	91	53	1795	17.5
4	116	81	2220	16.9
4	140	92	2572	14.9
4	101	83	2202	15.3
8	305	140	4215	13
8	304	120	3962	13.9
8	351	152	4215	12.8
6	250	105	3353	14.5
6	200	81	3012	17.6
4	85	52	2035	22.2
4	98	60	2164	22.1
6	225	100	3651	17.7
6	250	110	3645	16.2
6	258	95	3193	17.8
4	85	70	1990	17
4	97	75	2155	16.4
4	130	102	3150	15.7
8	318	150	3940	13.2
6	168	120	3820	16.7
8	350	180	4380	12.1
8	302	130	3870	15
8	318	150	3755	14
4	111	80	2155	14.8
4	79	58	1825	18.6
4	85	70	1945	16.8
8	305	145	3880	12.5
8	318	145	4140	13.7
6	231	105	3425	16.9
6	225	100	3630	17.7
8	400	180	4220	11.1
8	350	170	4165	11.4
8	351	149	4335	14.5
4	97	78	1940	14.5
4	97	75	2265	18.2
4	140	89	2755	15.8
4	98	83	2075	15.9
4	97	67	1985	16.4
6	146	97	2815	14.5
4	121	110	2600	12.8
4	90	48	1985	21.5
4	98	66	1800	14.4
4	85	70	2070	18.6
8	318	140	3735	13.2
8	302	139	3570	12.8
6	200	95	3155	18.2
6	200	85	2965	15.8
6	225	100	3430	17.2
6	232	90	3210	17.2
6	200	85	3070	16.7
6	225	110	3620	18.7
8	305	145	3425	13.2
6	231	165	3445	13.4
8	318	140	4080	13.7
4	98	68	2155	16.5
4	119	97	2300	14.7
4	105	75	2230	14.5
4	151	85	2855	17.6
5	131	103	2830	15.9
6	163	125	3140	13.6
6	163	133	3410	15.8
4	89	71	1990	14.9
4	98	68	2135	16.6
6	200	85	2990	18.2
4	140	88	2890	17.3
6	225	110	3360	16.6
8	305	130	3840	15.4
8	351	138	3955	13.2
8	318	135	3830	15.2
8	351	142	4054	14.3
8	267	125	3605	15
4	89	71	1925	14
4	86	65	1975	15.2
4	121	80	2670	15
4	141	71	3190	24.8
8	260	90	3420	22.2
4	105	70	2150	14.9
4	85	65	2020	19.2
4	151	90	2670	16
6	173	115	2595	11.3
4	151	90	2556	13.2
4	98	76	2144	14.7
4	98	70	2120	15.5
4	86	65	2019	16.4
4	140	88	2870	18.1
4	151	90	3003	20.1
4	97	78	2188	15.8
4	134	90	2711	15.5
4	119	92	2434	15
4	108	75	2265	15.2
4	156	105	2800	14.4
4	85	65	2110	19.2
5	121	67	2950	19.9
4	91	67	1850	13.8
4	89	62	1845	15.3
4	122	88	2500	15.1
4	135	84	2490	15.7
4	151	84	2635	16.4
6	173	110	2725	12.6
4	135	84	2385	12.9
4	86	64	1875	16.4
4	81	60	1760	16.1
4	85	65	1975	19.4
4	89	62	2050	17.3
4	105	63	2215	14.9
4	98	65	2045	16.2
4	105	74	2190	14.2
4	119	100	2615	14.8
4	141	80	3230	20.4
6	146	120	2930	13.8
6	231	110	3415	15.8
6	200	88	3060	17.1
6	225	85	3465	16.6
4	112	88	2640	18.6
4	112	88	2395	18
4	135	84	2525	16
4	151	90	2735	18
4	105	74	1980	15.3
4	91	68	1970	17.6
4	105	63	2125	14.7
4	120	88	2160	14.5
4	107	75	2205	14.5
4	91	67	1965	15.7
6	181	110	2945	16.4
6	262	85	3015	17
4	144	96	2665	13.9
4	151	90	2950	17.3
4	140	86	2790	15.6
4	135	84	2295	11.6
4	120	79	2625	18.6

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109704&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109704&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109704&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.7911
R-squared	0.6259
RMSE	1.6141

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7911 \tabularnewline
R-squared & 0.6259 \tabularnewline
RMSE & 1.6141 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109704&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7911[/C][/ROW]
[ROW][C]R-squared[/C][C]0.6259[/C][/ROW]
[ROW][C]RMSE[/C][C]1.6141[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109704&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109704&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goodness of Fit
Correlation	0.7911
R-squared	0.6259
RMSE	1.6141

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	11.5	12.6461538461538	-1.14615384615385
2	11	13.5877551020408	-2.58775510204082
3	10.5	13.5877551020408	-3.08775510204082
4	10	10.7214285714286	-0.721428571428572
5	8.5	10.7214285714286	-2.22142857142857
6	10	10.7214285714286	-0.721428571428572
7	10	10.7214285714286	-0.721428571428572
8	8	12.6461538461538	-4.64615384615385
9	10	10.7214285714286	-0.721428571428572
10	15	15.2875	-0.2875
11	15.5	15.2875	0.2125
12	20.5	19.6111111111111	0.88888888888889
13	17.5	15.2875	2.2125
14	17.5	15.2875	2.2125
15	12.5	13.5877551020408	-1.08775510204082
16	14	12.6461538461538	1.35384615384615
17	15	12.6461538461538	2.35384615384615
18	18.5	12.6461538461538	5.85384615384615
19	14.5	15.2875	-0.7875
20	14	15.2875	-1.2875
21	15.5	16.045	-0.545000000000002
22	15.5	16.045	-0.545000000000002
23	12	12.6461538461538	-0.646153846153846
24	13	13.5877551020408	-0.587755102040816
25	12	10.7214285714286	1.27857142857143
26	12	10.7214285714286	1.27857142857143
27	19	16.7848484848485	2.21515151515151
28	15	16.045	-1.045
29	14	15.2875	-1.2875
30	14	15.2875	-1.2875
31	14.5	15.2875	-0.7875
32	19	16.7848484848485	2.21515151515151
33	19	19.6111111111111	-0.611111111111111
34	20.5	16.7848484848485	3.71515151515151
35	17	15.2875	1.7125
36	16.5	15.2875	1.2125
37	12	12.6461538461538	-0.646153846153846
38	13.5	13.5877551020408	-0.0877551020408163
39	13	13.5877551020408	-0.587755102040816
40	11	10.7214285714286	0.278571428571428
41	13.5	13.5877551020408	-0.0877551020408163
42	12.5	10.7214285714286	1.77857142857143
43	13.5	15.2875	-1.7875
44	14	13.5877551020408	0.412244897959184
45	16	13.5877551020408	2.41224489795918
46	14.5	13.5877551020408	0.912244897959184
47	18	15.2875	2.7125
48	16	15.2875	0.7125
49	14.5	15.2875	-0.7875
50	15	15.2875	-0.2875
51	13	12.6461538461538	0.353846153846154
52	11.5	13.5877551020408	-2.08775510204082
53	14.5	13.5877551020408	0.912244897959184
54	12.5	13.5877551020408	-1.08775510204082
55	12	13.5877551020408	-1.58775510204082
56	13	13.5877551020408	-0.587755102040816
57	11	10.7214285714286	0.278571428571428
58	11	10.7214285714286	0.278571428571428
59	16.5	16.045	0.454999999999998
60	18	16.045	1.955
61	16.5	18.345	-1.845
62	16	16.045	-0.0450000000000017
63	14	13.5877551020408	0.412244897959184
64	12.5	12.6461538461538	-0.146153846153846
65	15	15.2875	-0.2875
66	19.5	16.7848484848485	2.71515151515151
67	16.5	15.2875	1.2125
68	18.5	15.2875	3.2125
69	14	15.2875	-1.2875
70	13	13.5877551020408	-0.587755102040816
71	9.5	10.7214285714286	-1.22142857142857
72	15.5	15.2875	0.2125
73	14	15.2875	-1.2875
74	11	13.5877551020408	-2.58775510204082
75	14	15.2875	-1.2875
76	11	12.6461538461538	-1.64615384615385
77	16.5	16.045	0.454999999999998
78	16	16.045	-0.0450000000000017
79	16.5	15.2875	1.2125
80	21	16.7848484848485	4.21515151515151
81	17	18.15	-1.15
82	18	18.15	-0.149999999999999
83	14	13.5877551020408	0.412244897959184
84	14.5	13.5877551020408	0.912244897959184
85	16	13.5877551020408	2.41224489795918
86	15.5	13.5877551020408	1.91224489795918
87	15.5	16.7848484848485	-1.28484848484849
88	14.5	15.2875	-0.7875
89	19	19.6111111111111	-0.611111111111111
90	14.5	15.2875	-0.7875
91	14	15.2875	-1.2875
92	15	15.2875	-0.2875
93	16	16.7848484848485	-0.784848484848485
94	16	16.045	-0.0450000000000017
95	19.5	18.345	1.155
96	11.5	10.7214285714286	0.778571428571428
97	14	13.5877551020408	0.412244897959184
98	13.5	13.5877551020408	-0.0877551020408163
99	21	18.15	2.85
100	19	18.15	0.850000000000001
101	19	18.15	0.850000000000001
102	13.5	16.045	-2.545
103	12	13.5877551020408	-1.58775510204082
104	17	15.2875	1.7125
105	16	16.045	-0.0450000000000017
106	13.5	15.2875	-1.7875
107	16.5	16.7848484848485	-0.284848484848485
108	14.5	16.045	-1.545
109	15	15.2875	-0.2875
110	17	18.345	-1.345
111	13.5	13.5877551020408	-0.0877551020408163
112	17.5	19.6111111111111	-2.11111111111111
113	16.9	15.2875	1.6125
114	14.9	15.2875	-0.387499999999999
115	15.3	15.2875	0.0125000000000011
116	13	13.5877551020408	-0.587755102040816
117	13.9	13.5877551020408	0.312244897959184
118	12.8	13.5877551020408	-0.787755102040816
119	14.5	16.045	-1.545
120	17.6	18.345	-0.744999999999997
121	22.2	19.6111111111111	2.58888888888889
122	22.1	19.6111111111111	2.48888888888889
123	17.7	18.15	-0.449999999999999
124	16.2	18.15	-1.95
125	17.8	16.045	1.755
126	17	16.7848484848485	0.215151515151515
127	16.4	15.2875	1.1125
128	15.7	16.045	-0.345000000000002
129	13.2	13.5877551020408	-0.387755102040817
130	16.7	13.5877551020408	3.11224489795918
131	12.1	12.6461538461538	-0.546153846153846
132	15	13.5877551020408	1.41224489795918
133	14	13.5877551020408	0.412244897959184
134	14.8	15.2875	-0.487499999999999
135	18.6	19.6111111111111	-1.01111111111111
136	16.8	16.7848484848485	0.0151515151515156
137	12.5	13.5877551020408	-1.08775510204082
138	13.7	13.5877551020408	0.112244897959183
139	16.9	16.045	0.854999999999997
140	17.7	18.15	-0.449999999999999
141	11.1	10.7214285714286	0.378571428571428
142	11.4	12.6461538461538	-1.24615384615385
143	14.5	13.5877551020408	0.912244897959184
144	14.5	15.2875	-0.7875
145	18.2	15.2875	2.9125
146	15.8	15.2875	0.512500000000001
147	15.9	15.2875	0.612500000000001
148	16.4	16.7848484848485	-0.384848484848487
149	14.5	15.2875	-0.7875
150	12.8	15.2875	-2.4875
151	21.5	19.6111111111111	1.88888888888889
152	14.4	16.7848484848485	-2.38484848484848
153	18.6	16.7848484848485	1.81515151515152
154	13.2	13.5877551020408	-0.387755102040817
155	12.8	13.5877551020408	-0.787755102040816
156	18.2	16.045	2.155
157	15.8	18.345	-2.545
158	17.2	18.15	-0.95
159	17.2	18.345	-1.145
160	16.7	18.345	-1.645
161	18.7	18.15	0.550000000000001
162	13.2	13.5877551020408	-0.387755102040817
163	13.4	12.6461538461538	0.753846153846155
164	13.7	13.5877551020408	0.112244897959183
165	16.5	16.7848484848485	-0.284848484848485
166	14.7	15.2875	-0.5875
167	14.5	15.2875	-0.7875
168	17.6	18.345	-0.744999999999997
169	15.9	15.2875	0.612500000000001
170	13.6	13.5877551020408	0.0122448979591834
171	15.8	13.5877551020408	2.21224489795918
172	14.9	16.7848484848485	-1.88484848484848
173	16.6	16.7848484848485	-0.184848484848484
174	18.2	18.345	-0.145
175	17.3	18.345	-1.045
176	16.6	16.045	0.555
177	15.4	13.5877551020408	1.81224489795918
178	13.2	13.5877551020408	-0.387755102040817
179	15.2	13.5877551020408	1.61224489795918
180	14.3	13.5877551020408	0.712244897959184
181	15	13.5877551020408	1.41224489795918
182	14	16.7848484848485	-2.78484848484849
183	15.2	16.7848484848485	-1.58484848484849
184	15	15.2875	-0.2875
185	24.8	18.345	6.455
186	22.2	18.345	3.855
187	14.9	16.7848484848485	-1.88484848484848
188	19.2	16.7848484848485	2.41515151515151
189	16	15.2875	0.7125
190	11.3	13.5877551020408	-2.28775510204082
191	13.2	15.2875	-2.0875
192	14.7	15.2875	-0.5875
193	15.5	16.7848484848485	-1.28484848484849
194	16.4	16.7848484848485	-0.384848484848487
195	18.1	18.345	-0.244999999999997
196	20.1	18.345	1.755
197	15.8	15.2875	0.512500000000001
198	15.5	15.2875	0.2125
199	15	15.2875	-0.2875
200	15.2	15.2875	-0.0875000000000004
201	14.4	15.2875	-0.8875
202	19.2	16.7848484848485	2.41515151515151
203	19.9	18.345	1.555
204	13.8	16.7848484848485	-2.98484848484848
205	15.3	16.7848484848485	-1.48484848484848
206	15.1	15.2875	-0.1875
207	15.7	15.2875	0.4125
208	16.4	15.2875	1.1125
209	12.6	15.2875	-2.6875
210	12.9	15.2875	-2.3875
211	16.4	16.7848484848485	-0.384848484848487
212	16.1	19.6111111111111	-3.51111111111111
213	19.4	16.7848484848485	2.61515151515151
214	17.3	16.7848484848485	0.515151515151516
215	14.9	16.7848484848485	-1.88484848484848
216	16.2	16.7848484848485	-0.584848484848486
217	14.2	15.2875	-1.0875
218	14.8	15.2875	-0.487499999999999
219	20.4	18.345	2.055
220	13.8	13.5877551020408	0.212244897959184
221	15.8	16.045	-0.245000000000001
222	17.1	18.345	-1.245
223	16.6	18.345	-1.745
224	18.6	15.2875	3.3125
225	18	15.2875	2.7125
226	16	15.2875	0.7125
227	18	15.2875	2.7125
228	15.3	15.2875	0.0125000000000011
229	17.6	16.7848484848485	0.815151515151516
230	14.7	16.7848484848485	-2.08484848484849
231	14.5	15.2875	-0.7875
232	14.5	15.2875	-0.7875
233	15.7	16.7848484848485	-1.08484848484849
234	16.4	16.045	0.354999999999997
235	17	18.345	-1.345
236	13.9	15.2875	-1.3875
237	17.3	18.345	-1.045
238	15.6	15.2875	0.3125
239	11.6	15.2875	-3.6875
240	18.6	15.2875	3.3125

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 11.5 & 12.6461538461538 & -1.14615384615385 \tabularnewline
2 & 11 & 13.5877551020408 & -2.58775510204082 \tabularnewline
3 & 10.5 & 13.5877551020408 & -3.08775510204082 \tabularnewline
4 & 10 & 10.7214285714286 & -0.721428571428572 \tabularnewline
5 & 8.5 & 10.7214285714286 & -2.22142857142857 \tabularnewline
6 & 10 & 10.7214285714286 & -0.721428571428572 \tabularnewline
7 & 10 & 10.7214285714286 & -0.721428571428572 \tabularnewline
8 & 8 & 12.6461538461538 & -4.64615384615385 \tabularnewline
9 & 10 & 10.7214285714286 & -0.721428571428572 \tabularnewline
10 & 15 & 15.2875 & -0.2875 \tabularnewline
11 & 15.5 & 15.2875 & 0.2125 \tabularnewline
12 & 20.5 & 19.6111111111111 & 0.88888888888889 \tabularnewline
13 & 17.5 & 15.2875 & 2.2125 \tabularnewline
14 & 17.5 & 15.2875 & 2.2125 \tabularnewline
15 & 12.5 & 13.5877551020408 & -1.08775510204082 \tabularnewline
16 & 14 & 12.6461538461538 & 1.35384615384615 \tabularnewline
17 & 15 & 12.6461538461538 & 2.35384615384615 \tabularnewline
18 & 18.5 & 12.6461538461538 & 5.85384615384615 \tabularnewline
19 & 14.5 & 15.2875 & -0.7875 \tabularnewline
20 & 14 & 15.2875 & -1.2875 \tabularnewline
21 & 15.5 & 16.045 & -0.545000000000002 \tabularnewline
22 & 15.5 & 16.045 & -0.545000000000002 \tabularnewline
23 & 12 & 12.6461538461538 & -0.646153846153846 \tabularnewline
24 & 13 & 13.5877551020408 & -0.587755102040816 \tabularnewline
25 & 12 & 10.7214285714286 & 1.27857142857143 \tabularnewline
26 & 12 & 10.7214285714286 & 1.27857142857143 \tabularnewline
27 & 19 & 16.7848484848485 & 2.21515151515151 \tabularnewline
28 & 15 & 16.045 & -1.045 \tabularnewline
29 & 14 & 15.2875 & -1.2875 \tabularnewline
30 & 14 & 15.2875 & -1.2875 \tabularnewline
31 & 14.5 & 15.2875 & -0.7875 \tabularnewline
32 & 19 & 16.7848484848485 & 2.21515151515151 \tabularnewline
33 & 19 & 19.6111111111111 & -0.611111111111111 \tabularnewline
34 & 20.5 & 16.7848484848485 & 3.71515151515151 \tabularnewline
35 & 17 & 15.2875 & 1.7125 \tabularnewline
36 & 16.5 & 15.2875 & 1.2125 \tabularnewline
37 & 12 & 12.6461538461538 & -0.646153846153846 \tabularnewline
38 & 13.5 & 13.5877551020408 & -0.0877551020408163 \tabularnewline
39 & 13 & 13.5877551020408 & -0.587755102040816 \tabularnewline
40 & 11 & 10.7214285714286 & 0.278571428571428 \tabularnewline
41 & 13.5 & 13.5877551020408 & -0.0877551020408163 \tabularnewline
42 & 12.5 & 10.7214285714286 & 1.77857142857143 \tabularnewline
43 & 13.5 & 15.2875 & -1.7875 \tabularnewline
44 & 14 & 13.5877551020408 & 0.412244897959184 \tabularnewline
45 & 16 & 13.5877551020408 & 2.41224489795918 \tabularnewline
46 & 14.5 & 13.5877551020408 & 0.912244897959184 \tabularnewline
47 & 18 & 15.2875 & 2.7125 \tabularnewline
48 & 16 & 15.2875 & 0.7125 \tabularnewline
49 & 14.5 & 15.2875 & -0.7875 \tabularnewline
50 & 15 & 15.2875 & -0.2875 \tabularnewline
51 & 13 & 12.6461538461538 & 0.353846153846154 \tabularnewline
52 & 11.5 & 13.5877551020408 & -2.08775510204082 \tabularnewline
53 & 14.5 & 13.5877551020408 & 0.912244897959184 \tabularnewline
54 & 12.5 & 13.5877551020408 & -1.08775510204082 \tabularnewline
55 & 12 & 13.5877551020408 & -1.58775510204082 \tabularnewline
56 & 13 & 13.5877551020408 & -0.587755102040816 \tabularnewline
57 & 11 & 10.7214285714286 & 0.278571428571428 \tabularnewline
58 & 11 & 10.7214285714286 & 0.278571428571428 \tabularnewline
59 & 16.5 & 16.045 & 0.454999999999998 \tabularnewline
60 & 18 & 16.045 & 1.955 \tabularnewline
61 & 16.5 & 18.345 & -1.845 \tabularnewline
62 & 16 & 16.045 & -0.0450000000000017 \tabularnewline
63 & 14 & 13.5877551020408 & 0.412244897959184 \tabularnewline
64 & 12.5 & 12.6461538461538 & -0.146153846153846 \tabularnewline
65 & 15 & 15.2875 & -0.2875 \tabularnewline
66 & 19.5 & 16.7848484848485 & 2.71515151515151 \tabularnewline
67 & 16.5 & 15.2875 & 1.2125 \tabularnewline
68 & 18.5 & 15.2875 & 3.2125 \tabularnewline
69 & 14 & 15.2875 & -1.2875 \tabularnewline
70 & 13 & 13.5877551020408 & -0.587755102040816 \tabularnewline
71 & 9.5 & 10.7214285714286 & -1.22142857142857 \tabularnewline
72 & 15.5 & 15.2875 & 0.2125 \tabularnewline
73 & 14 & 15.2875 & -1.2875 \tabularnewline
74 & 11 & 13.5877551020408 & -2.58775510204082 \tabularnewline
75 & 14 & 15.2875 & -1.2875 \tabularnewline
76 & 11 & 12.6461538461538 & -1.64615384615385 \tabularnewline
77 & 16.5 & 16.045 & 0.454999999999998 \tabularnewline
78 & 16 & 16.045 & -0.0450000000000017 \tabularnewline
79 & 16.5 & 15.2875 & 1.2125 \tabularnewline
80 & 21 & 16.7848484848485 & 4.21515151515151 \tabularnewline
81 & 17 & 18.15 & -1.15 \tabularnewline
82 & 18 & 18.15 & -0.149999999999999 \tabularnewline
83 & 14 & 13.5877551020408 & 0.412244897959184 \tabularnewline
84 & 14.5 & 13.5877551020408 & 0.912244897959184 \tabularnewline
85 & 16 & 13.5877551020408 & 2.41224489795918 \tabularnewline
86 & 15.5 & 13.5877551020408 & 1.91224489795918 \tabularnewline
87 & 15.5 & 16.7848484848485 & -1.28484848484849 \tabularnewline
88 & 14.5 & 15.2875 & -0.7875 \tabularnewline
89 & 19 & 19.6111111111111 & -0.611111111111111 \tabularnewline
90 & 14.5 & 15.2875 & -0.7875 \tabularnewline
91 & 14 & 15.2875 & -1.2875 \tabularnewline
92 & 15 & 15.2875 & -0.2875 \tabularnewline
93 & 16 & 16.7848484848485 & -0.784848484848485 \tabularnewline
94 & 16 & 16.045 & -0.0450000000000017 \tabularnewline
95 & 19.5 & 18.345 & 1.155 \tabularnewline
96 & 11.5 & 10.7214285714286 & 0.778571428571428 \tabularnewline
97 & 14 & 13.5877551020408 & 0.412244897959184 \tabularnewline
98 & 13.5 & 13.5877551020408 & -0.0877551020408163 \tabularnewline
99 & 21 & 18.15 & 2.85 \tabularnewline
100 & 19 & 18.15 & 0.850000000000001 \tabularnewline
101 & 19 & 18.15 & 0.850000000000001 \tabularnewline
102 & 13.5 & 16.045 & -2.545 \tabularnewline
103 & 12 & 13.5877551020408 & -1.58775510204082 \tabularnewline
104 & 17 & 15.2875 & 1.7125 \tabularnewline
105 & 16 & 16.045 & -0.0450000000000017 \tabularnewline
106 & 13.5 & 15.2875 & -1.7875 \tabularnewline
107 & 16.5 & 16.7848484848485 & -0.284848484848485 \tabularnewline
108 & 14.5 & 16.045 & -1.545 \tabularnewline
109 & 15 & 15.2875 & -0.2875 \tabularnewline
110 & 17 & 18.345 & -1.345 \tabularnewline
111 & 13.5 & 13.5877551020408 & -0.0877551020408163 \tabularnewline
112 & 17.5 & 19.6111111111111 & -2.11111111111111 \tabularnewline
113 & 16.9 & 15.2875 & 1.6125 \tabularnewline
114 & 14.9 & 15.2875 & -0.387499999999999 \tabularnewline
115 & 15.3 & 15.2875 & 0.0125000000000011 \tabularnewline
116 & 13 & 13.5877551020408 & -0.587755102040816 \tabularnewline
117 & 13.9 & 13.5877551020408 & 0.312244897959184 \tabularnewline
118 & 12.8 & 13.5877551020408 & -0.787755102040816 \tabularnewline
119 & 14.5 & 16.045 & -1.545 \tabularnewline
120 & 17.6 & 18.345 & -0.744999999999997 \tabularnewline
121 & 22.2 & 19.6111111111111 & 2.58888888888889 \tabularnewline
122 & 22.1 & 19.6111111111111 & 2.48888888888889 \tabularnewline
123 & 17.7 & 18.15 & -0.449999999999999 \tabularnewline
124 & 16.2 & 18.15 & -1.95 \tabularnewline
125 & 17.8 & 16.045 & 1.755 \tabularnewline
126 & 17 & 16.7848484848485 & 0.215151515151515 \tabularnewline
127 & 16.4 & 15.2875 & 1.1125 \tabularnewline
128 & 15.7 & 16.045 & -0.345000000000002 \tabularnewline
129 & 13.2 & 13.5877551020408 & -0.387755102040817 \tabularnewline
130 & 16.7 & 13.5877551020408 & 3.11224489795918 \tabularnewline
131 & 12.1 & 12.6461538461538 & -0.546153846153846 \tabularnewline
132 & 15 & 13.5877551020408 & 1.41224489795918 \tabularnewline
133 & 14 & 13.5877551020408 & 0.412244897959184 \tabularnewline
134 & 14.8 & 15.2875 & -0.487499999999999 \tabularnewline
135 & 18.6 & 19.6111111111111 & -1.01111111111111 \tabularnewline
136 & 16.8 & 16.7848484848485 & 0.0151515151515156 \tabularnewline
137 & 12.5 & 13.5877551020408 & -1.08775510204082 \tabularnewline
138 & 13.7 & 13.5877551020408 & 0.112244897959183 \tabularnewline
139 & 16.9 & 16.045 & 0.854999999999997 \tabularnewline
140 & 17.7 & 18.15 & -0.449999999999999 \tabularnewline
141 & 11.1 & 10.7214285714286 & 0.378571428571428 \tabularnewline
142 & 11.4 & 12.6461538461538 & -1.24615384615385 \tabularnewline
143 & 14.5 & 13.5877551020408 & 0.912244897959184 \tabularnewline
144 & 14.5 & 15.2875 & -0.7875 \tabularnewline
145 & 18.2 & 15.2875 & 2.9125 \tabularnewline
146 & 15.8 & 15.2875 & 0.512500000000001 \tabularnewline
147 & 15.9 & 15.2875 & 0.612500000000001 \tabularnewline
148 & 16.4 & 16.7848484848485 & -0.384848484848487 \tabularnewline
149 & 14.5 & 15.2875 & -0.7875 \tabularnewline
150 & 12.8 & 15.2875 & -2.4875 \tabularnewline
151 & 21.5 & 19.6111111111111 & 1.88888888888889 \tabularnewline
152 & 14.4 & 16.7848484848485 & -2.38484848484848 \tabularnewline
153 & 18.6 & 16.7848484848485 & 1.81515151515152 \tabularnewline
154 & 13.2 & 13.5877551020408 & -0.387755102040817 \tabularnewline
155 & 12.8 & 13.5877551020408 & -0.787755102040816 \tabularnewline
156 & 18.2 & 16.045 & 2.155 \tabularnewline
157 & 15.8 & 18.345 & -2.545 \tabularnewline
158 & 17.2 & 18.15 & -0.95 \tabularnewline
159 & 17.2 & 18.345 & -1.145 \tabularnewline
160 & 16.7 & 18.345 & -1.645 \tabularnewline
161 & 18.7 & 18.15 & 0.550000000000001 \tabularnewline
162 & 13.2 & 13.5877551020408 & -0.387755102040817 \tabularnewline
163 & 13.4 & 12.6461538461538 & 0.753846153846155 \tabularnewline
164 & 13.7 & 13.5877551020408 & 0.112244897959183 \tabularnewline
165 & 16.5 & 16.7848484848485 & -0.284848484848485 \tabularnewline
166 & 14.7 & 15.2875 & -0.5875 \tabularnewline
167 & 14.5 & 15.2875 & -0.7875 \tabularnewline
168 & 17.6 & 18.345 & -0.744999999999997 \tabularnewline
169 & 15.9 & 15.2875 & 0.612500000000001 \tabularnewline
170 & 13.6 & 13.5877551020408 & 0.0122448979591834 \tabularnewline
171 & 15.8 & 13.5877551020408 & 2.21224489795918 \tabularnewline
172 & 14.9 & 16.7848484848485 & -1.88484848484848 \tabularnewline
173 & 16.6 & 16.7848484848485 & -0.184848484848484 \tabularnewline
174 & 18.2 & 18.345 & -0.145 \tabularnewline
175 & 17.3 & 18.345 & -1.045 \tabularnewline
176 & 16.6 & 16.045 & 0.555 \tabularnewline
177 & 15.4 & 13.5877551020408 & 1.81224489795918 \tabularnewline
178 & 13.2 & 13.5877551020408 & -0.387755102040817 \tabularnewline
179 & 15.2 & 13.5877551020408 & 1.61224489795918 \tabularnewline
180 & 14.3 & 13.5877551020408 & 0.712244897959184 \tabularnewline
181 & 15 & 13.5877551020408 & 1.41224489795918 \tabularnewline
182 & 14 & 16.7848484848485 & -2.78484848484849 \tabularnewline
183 & 15.2 & 16.7848484848485 & -1.58484848484849 \tabularnewline
184 & 15 & 15.2875 & -0.2875 \tabularnewline
185 & 24.8 & 18.345 & 6.455 \tabularnewline
186 & 22.2 & 18.345 & 3.855 \tabularnewline
187 & 14.9 & 16.7848484848485 & -1.88484848484848 \tabularnewline
188 & 19.2 & 16.7848484848485 & 2.41515151515151 \tabularnewline
189 & 16 & 15.2875 & 0.7125 \tabularnewline
190 & 11.3 & 13.5877551020408 & -2.28775510204082 \tabularnewline
191 & 13.2 & 15.2875 & -2.0875 \tabularnewline
192 & 14.7 & 15.2875 & -0.5875 \tabularnewline
193 & 15.5 & 16.7848484848485 & -1.28484848484849 \tabularnewline
194 & 16.4 & 16.7848484848485 & -0.384848484848487 \tabularnewline
195 & 18.1 & 18.345 & -0.244999999999997 \tabularnewline
196 & 20.1 & 18.345 & 1.755 \tabularnewline
197 & 15.8 & 15.2875 & 0.512500000000001 \tabularnewline
198 & 15.5 & 15.2875 & 0.2125 \tabularnewline
199 & 15 & 15.2875 & -0.2875 \tabularnewline
200 & 15.2 & 15.2875 & -0.0875000000000004 \tabularnewline
201 & 14.4 & 15.2875 & -0.8875 \tabularnewline
202 & 19.2 & 16.7848484848485 & 2.41515151515151 \tabularnewline
203 & 19.9 & 18.345 & 1.555 \tabularnewline
204 & 13.8 & 16.7848484848485 & -2.98484848484848 \tabularnewline
205 & 15.3 & 16.7848484848485 & -1.48484848484848 \tabularnewline
206 & 15.1 & 15.2875 & -0.1875 \tabularnewline
207 & 15.7 & 15.2875 & 0.4125 \tabularnewline
208 & 16.4 & 15.2875 & 1.1125 \tabularnewline
209 & 12.6 & 15.2875 & -2.6875 \tabularnewline
210 & 12.9 & 15.2875 & -2.3875 \tabularnewline
211 & 16.4 & 16.7848484848485 & -0.384848484848487 \tabularnewline
212 & 16.1 & 19.6111111111111 & -3.51111111111111 \tabularnewline
213 & 19.4 & 16.7848484848485 & 2.61515151515151 \tabularnewline
214 & 17.3 & 16.7848484848485 & 0.515151515151516 \tabularnewline
215 & 14.9 & 16.7848484848485 & -1.88484848484848 \tabularnewline
216 & 16.2 & 16.7848484848485 & -0.584848484848486 \tabularnewline
217 & 14.2 & 15.2875 & -1.0875 \tabularnewline
218 & 14.8 & 15.2875 & -0.487499999999999 \tabularnewline
219 & 20.4 & 18.345 & 2.055 \tabularnewline
220 & 13.8 & 13.5877551020408 & 0.212244897959184 \tabularnewline
221 & 15.8 & 16.045 & -0.245000000000001 \tabularnewline
222 & 17.1 & 18.345 & -1.245 \tabularnewline
223 & 16.6 & 18.345 & -1.745 \tabularnewline
224 & 18.6 & 15.2875 & 3.3125 \tabularnewline
225 & 18 & 15.2875 & 2.7125 \tabularnewline
226 & 16 & 15.2875 & 0.7125 \tabularnewline
227 & 18 & 15.2875 & 2.7125 \tabularnewline
228 & 15.3 & 15.2875 & 0.0125000000000011 \tabularnewline
229 & 17.6 & 16.7848484848485 & 0.815151515151516 \tabularnewline
230 & 14.7 & 16.7848484848485 & -2.08484848484849 \tabularnewline
231 & 14.5 & 15.2875 & -0.7875 \tabularnewline
232 & 14.5 & 15.2875 & -0.7875 \tabularnewline
233 & 15.7 & 16.7848484848485 & -1.08484848484849 \tabularnewline
234 & 16.4 & 16.045 & 0.354999999999997 \tabularnewline
235 & 17 & 18.345 & -1.345 \tabularnewline
236 & 13.9 & 15.2875 & -1.3875 \tabularnewline
237 & 17.3 & 18.345 & -1.045 \tabularnewline
238 & 15.6 & 15.2875 & 0.3125 \tabularnewline
239 & 11.6 & 15.2875 & -3.6875 \tabularnewline
240 & 18.6 & 15.2875 & 3.3125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109704&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]11.5[/C][C]12.6461538461538[/C][C]-1.14615384615385[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]13.5877551020408[/C][C]-2.58775510204082[/C][/ROW]
[ROW][C]3[/C][C]10.5[/C][C]13.5877551020408[/C][C]-3.08775510204082[/C][/ROW]
[ROW][C]4[/C][C]10[/C][C]10.7214285714286[/C][C]-0.721428571428572[/C][/ROW]
[ROW][C]5[/C][C]8.5[/C][C]10.7214285714286[/C][C]-2.22142857142857[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]10.7214285714286[/C][C]-0.721428571428572[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]10.7214285714286[/C][C]-0.721428571428572[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]12.6461538461538[/C][C]-4.64615384615385[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]10.7214285714286[/C][C]-0.721428571428572[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.2875[/C][C]-0.2875[/C][/ROW]
[ROW][C]11[/C][C]15.5[/C][C]15.2875[/C][C]0.2125[/C][/ROW]
[ROW][C]12[/C][C]20.5[/C][C]19.6111111111111[/C][C]0.88888888888889[/C][/ROW]
[ROW][C]13[/C][C]17.5[/C][C]15.2875[/C][C]2.2125[/C][/ROW]
[ROW][C]14[/C][C]17.5[/C][C]15.2875[/C][C]2.2125[/C][/ROW]
[ROW][C]15[/C][C]12.5[/C][C]13.5877551020408[/C][C]-1.08775510204082[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.6461538461538[/C][C]1.35384615384615[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]12.6461538461538[/C][C]2.35384615384615[/C][/ROW]
[ROW][C]18[/C][C]18.5[/C][C]12.6461538461538[/C][C]5.85384615384615[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]20[/C][C]14[/C][C]15.2875[/C][C]-1.2875[/C][/ROW]
[ROW][C]21[/C][C]15.5[/C][C]16.045[/C][C]-0.545000000000002[/C][/ROW]
[ROW][C]22[/C][C]15.5[/C][C]16.045[/C][C]-0.545000000000002[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.6461538461538[/C][C]-0.646153846153846[/C][/ROW]
[ROW][C]24[/C][C]13[/C][C]13.5877551020408[/C][C]-0.587755102040816[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]10.7214285714286[/C][C]1.27857142857143[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]10.7214285714286[/C][C]1.27857142857143[/C][/ROW]
[ROW][C]27[/C][C]19[/C][C]16.7848484848485[/C][C]2.21515151515151[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.045[/C][C]-1.045[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]15.2875[/C][C]-1.2875[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]15.2875[/C][C]-1.2875[/C][/ROW]
[ROW][C]31[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]32[/C][C]19[/C][C]16.7848484848485[/C][C]2.21515151515151[/C][/ROW]
[ROW][C]33[/C][C]19[/C][C]19.6111111111111[/C][C]-0.611111111111111[/C][/ROW]
[ROW][C]34[/C][C]20.5[/C][C]16.7848484848485[/C][C]3.71515151515151[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]15.2875[/C][C]1.7125[/C][/ROW]
[ROW][C]36[/C][C]16.5[/C][C]15.2875[/C][C]1.2125[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.6461538461538[/C][C]-0.646153846153846[/C][/ROW]
[ROW][C]38[/C][C]13.5[/C][C]13.5877551020408[/C][C]-0.0877551020408163[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.5877551020408[/C][C]-0.587755102040816[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]10.7214285714286[/C][C]0.278571428571428[/C][/ROW]
[ROW][C]41[/C][C]13.5[/C][C]13.5877551020408[/C][C]-0.0877551020408163[/C][/ROW]
[ROW][C]42[/C][C]12.5[/C][C]10.7214285714286[/C][C]1.77857142857143[/C][/ROW]
[ROW][C]43[/C][C]13.5[/C][C]15.2875[/C][C]-1.7875[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.5877551020408[/C][C]0.412244897959184[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]13.5877551020408[/C][C]2.41224489795918[/C][/ROW]
[ROW][C]46[/C][C]14.5[/C][C]13.5877551020408[/C][C]0.912244897959184[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]15.2875[/C][C]2.7125[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]15.2875[/C][C]0.7125[/C][/ROW]
[ROW][C]49[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]50[/C][C]15[/C][C]15.2875[/C][C]-0.2875[/C][/ROW]
[ROW][C]51[/C][C]13[/C][C]12.6461538461538[/C][C]0.353846153846154[/C][/ROW]
[ROW][C]52[/C][C]11.5[/C][C]13.5877551020408[/C][C]-2.08775510204082[/C][/ROW]
[ROW][C]53[/C][C]14.5[/C][C]13.5877551020408[/C][C]0.912244897959184[/C][/ROW]
[ROW][C]54[/C][C]12.5[/C][C]13.5877551020408[/C][C]-1.08775510204082[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.5877551020408[/C][C]-1.58775510204082[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]13.5877551020408[/C][C]-0.587755102040816[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.7214285714286[/C][C]0.278571428571428[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]10.7214285714286[/C][C]0.278571428571428[/C][/ROW]
[ROW][C]59[/C][C]16.5[/C][C]16.045[/C][C]0.454999999999998[/C][/ROW]
[ROW][C]60[/C][C]18[/C][C]16.045[/C][C]1.955[/C][/ROW]
[ROW][C]61[/C][C]16.5[/C][C]18.345[/C][C]-1.845[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.045[/C][C]-0.0450000000000017[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.5877551020408[/C][C]0.412244897959184[/C][/ROW]
[ROW][C]64[/C][C]12.5[/C][C]12.6461538461538[/C][C]-0.146153846153846[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]15.2875[/C][C]-0.2875[/C][/ROW]
[ROW][C]66[/C][C]19.5[/C][C]16.7848484848485[/C][C]2.71515151515151[/C][/ROW]
[ROW][C]67[/C][C]16.5[/C][C]15.2875[/C][C]1.2125[/C][/ROW]
[ROW][C]68[/C][C]18.5[/C][C]15.2875[/C][C]3.2125[/C][/ROW]
[ROW][C]69[/C][C]14[/C][C]15.2875[/C][C]-1.2875[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]13.5877551020408[/C][C]-0.587755102040816[/C][/ROW]
[ROW][C]71[/C][C]9.5[/C][C]10.7214285714286[/C][C]-1.22142857142857[/C][/ROW]
[ROW][C]72[/C][C]15.5[/C][C]15.2875[/C][C]0.2125[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]15.2875[/C][C]-1.2875[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]13.5877551020408[/C][C]-2.58775510204082[/C][/ROW]
[ROW][C]75[/C][C]14[/C][C]15.2875[/C][C]-1.2875[/C][/ROW]
[ROW][C]76[/C][C]11[/C][C]12.6461538461538[/C][C]-1.64615384615385[/C][/ROW]
[ROW][C]77[/C][C]16.5[/C][C]16.045[/C][C]0.454999999999998[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]16.045[/C][C]-0.0450000000000017[/C][/ROW]
[ROW][C]79[/C][C]16.5[/C][C]15.2875[/C][C]1.2125[/C][/ROW]
[ROW][C]80[/C][C]21[/C][C]16.7848484848485[/C][C]4.21515151515151[/C][/ROW]
[ROW][C]81[/C][C]17[/C][C]18.15[/C][C]-1.15[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]18.15[/C][C]-0.149999999999999[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.5877551020408[/C][C]0.412244897959184[/C][/ROW]
[ROW][C]84[/C][C]14.5[/C][C]13.5877551020408[/C][C]0.912244897959184[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]13.5877551020408[/C][C]2.41224489795918[/C][/ROW]
[ROW][C]86[/C][C]15.5[/C][C]13.5877551020408[/C][C]1.91224489795918[/C][/ROW]
[ROW][C]87[/C][C]15.5[/C][C]16.7848484848485[/C][C]-1.28484848484849[/C][/ROW]
[ROW][C]88[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]89[/C][C]19[/C][C]19.6111111111111[/C][C]-0.611111111111111[/C][/ROW]
[ROW][C]90[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]15.2875[/C][C]-1.2875[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]15.2875[/C][C]-0.2875[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]16.7848484848485[/C][C]-0.784848484848485[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]16.045[/C][C]-0.0450000000000017[/C][/ROW]
[ROW][C]95[/C][C]19.5[/C][C]18.345[/C][C]1.155[/C][/ROW]
[ROW][C]96[/C][C]11.5[/C][C]10.7214285714286[/C][C]0.778571428571428[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]13.5877551020408[/C][C]0.412244897959184[/C][/ROW]
[ROW][C]98[/C][C]13.5[/C][C]13.5877551020408[/C][C]-0.0877551020408163[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]18.15[/C][C]2.85[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]18.15[/C][C]0.850000000000001[/C][/ROW]
[ROW][C]101[/C][C]19[/C][C]18.15[/C][C]0.850000000000001[/C][/ROW]
[ROW][C]102[/C][C]13.5[/C][C]16.045[/C][C]-2.545[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]13.5877551020408[/C][C]-1.58775510204082[/C][/ROW]
[ROW][C]104[/C][C]17[/C][C]15.2875[/C][C]1.7125[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]16.045[/C][C]-0.0450000000000017[/C][/ROW]
[ROW][C]106[/C][C]13.5[/C][C]15.2875[/C][C]-1.7875[/C][/ROW]
[ROW][C]107[/C][C]16.5[/C][C]16.7848484848485[/C][C]-0.284848484848485[/C][/ROW]
[ROW][C]108[/C][C]14.5[/C][C]16.045[/C][C]-1.545[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.2875[/C][C]-0.2875[/C][/ROW]
[ROW][C]110[/C][C]17[/C][C]18.345[/C][C]-1.345[/C][/ROW]
[ROW][C]111[/C][C]13.5[/C][C]13.5877551020408[/C][C]-0.0877551020408163[/C][/ROW]
[ROW][C]112[/C][C]17.5[/C][C]19.6111111111111[/C][C]-2.11111111111111[/C][/ROW]
[ROW][C]113[/C][C]16.9[/C][C]15.2875[/C][C]1.6125[/C][/ROW]
[ROW][C]114[/C][C]14.9[/C][C]15.2875[/C][C]-0.387499999999999[/C][/ROW]
[ROW][C]115[/C][C]15.3[/C][C]15.2875[/C][C]0.0125000000000011[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]13.5877551020408[/C][C]-0.587755102040816[/C][/ROW]
[ROW][C]117[/C][C]13.9[/C][C]13.5877551020408[/C][C]0.312244897959184[/C][/ROW]
[ROW][C]118[/C][C]12.8[/C][C]13.5877551020408[/C][C]-0.787755102040816[/C][/ROW]
[ROW][C]119[/C][C]14.5[/C][C]16.045[/C][C]-1.545[/C][/ROW]
[ROW][C]120[/C][C]17.6[/C][C]18.345[/C][C]-0.744999999999997[/C][/ROW]
[ROW][C]121[/C][C]22.2[/C][C]19.6111111111111[/C][C]2.58888888888889[/C][/ROW]
[ROW][C]122[/C][C]22.1[/C][C]19.6111111111111[/C][C]2.48888888888889[/C][/ROW]
[ROW][C]123[/C][C]17.7[/C][C]18.15[/C][C]-0.449999999999999[/C][/ROW]
[ROW][C]124[/C][C]16.2[/C][C]18.15[/C][C]-1.95[/C][/ROW]
[ROW][C]125[/C][C]17.8[/C][C]16.045[/C][C]1.755[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]16.7848484848485[/C][C]0.215151515151515[/C][/ROW]
[ROW][C]127[/C][C]16.4[/C][C]15.2875[/C][C]1.1125[/C][/ROW]
[ROW][C]128[/C][C]15.7[/C][C]16.045[/C][C]-0.345000000000002[/C][/ROW]
[ROW][C]129[/C][C]13.2[/C][C]13.5877551020408[/C][C]-0.387755102040817[/C][/ROW]
[ROW][C]130[/C][C]16.7[/C][C]13.5877551020408[/C][C]3.11224489795918[/C][/ROW]
[ROW][C]131[/C][C]12.1[/C][C]12.6461538461538[/C][C]-0.546153846153846[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]13.5877551020408[/C][C]1.41224489795918[/C][/ROW]
[ROW][C]133[/C][C]14[/C][C]13.5877551020408[/C][C]0.412244897959184[/C][/ROW]
[ROW][C]134[/C][C]14.8[/C][C]15.2875[/C][C]-0.487499999999999[/C][/ROW]
[ROW][C]135[/C][C]18.6[/C][C]19.6111111111111[/C][C]-1.01111111111111[/C][/ROW]
[ROW][C]136[/C][C]16.8[/C][C]16.7848484848485[/C][C]0.0151515151515156[/C][/ROW]
[ROW][C]137[/C][C]12.5[/C][C]13.5877551020408[/C][C]-1.08775510204082[/C][/ROW]
[ROW][C]138[/C][C]13.7[/C][C]13.5877551020408[/C][C]0.112244897959183[/C][/ROW]
[ROW][C]139[/C][C]16.9[/C][C]16.045[/C][C]0.854999999999997[/C][/ROW]
[ROW][C]140[/C][C]17.7[/C][C]18.15[/C][C]-0.449999999999999[/C][/ROW]
[ROW][C]141[/C][C]11.1[/C][C]10.7214285714286[/C][C]0.378571428571428[/C][/ROW]
[ROW][C]142[/C][C]11.4[/C][C]12.6461538461538[/C][C]-1.24615384615385[/C][/ROW]
[ROW][C]143[/C][C]14.5[/C][C]13.5877551020408[/C][C]0.912244897959184[/C][/ROW]
[ROW][C]144[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]145[/C][C]18.2[/C][C]15.2875[/C][C]2.9125[/C][/ROW]
[ROW][C]146[/C][C]15.8[/C][C]15.2875[/C][C]0.512500000000001[/C][/ROW]
[ROW][C]147[/C][C]15.9[/C][C]15.2875[/C][C]0.612500000000001[/C][/ROW]
[ROW][C]148[/C][C]16.4[/C][C]16.7848484848485[/C][C]-0.384848484848487[/C][/ROW]
[ROW][C]149[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]150[/C][C]12.8[/C][C]15.2875[/C][C]-2.4875[/C][/ROW]
[ROW][C]151[/C][C]21.5[/C][C]19.6111111111111[/C][C]1.88888888888889[/C][/ROW]
[ROW][C]152[/C][C]14.4[/C][C]16.7848484848485[/C][C]-2.38484848484848[/C][/ROW]
[ROW][C]153[/C][C]18.6[/C][C]16.7848484848485[/C][C]1.81515151515152[/C][/ROW]
[ROW][C]154[/C][C]13.2[/C][C]13.5877551020408[/C][C]-0.387755102040817[/C][/ROW]
[ROW][C]155[/C][C]12.8[/C][C]13.5877551020408[/C][C]-0.787755102040816[/C][/ROW]
[ROW][C]156[/C][C]18.2[/C][C]16.045[/C][C]2.155[/C][/ROW]
[ROW][C]157[/C][C]15.8[/C][C]18.345[/C][C]-2.545[/C][/ROW]
[ROW][C]158[/C][C]17.2[/C][C]18.15[/C][C]-0.95[/C][/ROW]
[ROW][C]159[/C][C]17.2[/C][C]18.345[/C][C]-1.145[/C][/ROW]
[ROW][C]160[/C][C]16.7[/C][C]18.345[/C][C]-1.645[/C][/ROW]
[ROW][C]161[/C][C]18.7[/C][C]18.15[/C][C]0.550000000000001[/C][/ROW]
[ROW][C]162[/C][C]13.2[/C][C]13.5877551020408[/C][C]-0.387755102040817[/C][/ROW]
[ROW][C]163[/C][C]13.4[/C][C]12.6461538461538[/C][C]0.753846153846155[/C][/ROW]
[ROW][C]164[/C][C]13.7[/C][C]13.5877551020408[/C][C]0.112244897959183[/C][/ROW]
[ROW][C]165[/C][C]16.5[/C][C]16.7848484848485[/C][C]-0.284848484848485[/C][/ROW]
[ROW][C]166[/C][C]14.7[/C][C]15.2875[/C][C]-0.5875[/C][/ROW]
[ROW][C]167[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]168[/C][C]17.6[/C][C]18.345[/C][C]-0.744999999999997[/C][/ROW]
[ROW][C]169[/C][C]15.9[/C][C]15.2875[/C][C]0.612500000000001[/C][/ROW]
[ROW][C]170[/C][C]13.6[/C][C]13.5877551020408[/C][C]0.0122448979591834[/C][/ROW]
[ROW][C]171[/C][C]15.8[/C][C]13.5877551020408[/C][C]2.21224489795918[/C][/ROW]
[ROW][C]172[/C][C]14.9[/C][C]16.7848484848485[/C][C]-1.88484848484848[/C][/ROW]
[ROW][C]173[/C][C]16.6[/C][C]16.7848484848485[/C][C]-0.184848484848484[/C][/ROW]
[ROW][C]174[/C][C]18.2[/C][C]18.345[/C][C]-0.145[/C][/ROW]
[ROW][C]175[/C][C]17.3[/C][C]18.345[/C][C]-1.045[/C][/ROW]
[ROW][C]176[/C][C]16.6[/C][C]16.045[/C][C]0.555[/C][/ROW]
[ROW][C]177[/C][C]15.4[/C][C]13.5877551020408[/C][C]1.81224489795918[/C][/ROW]
[ROW][C]178[/C][C]13.2[/C][C]13.5877551020408[/C][C]-0.387755102040817[/C][/ROW]
[ROW][C]179[/C][C]15.2[/C][C]13.5877551020408[/C][C]1.61224489795918[/C][/ROW]
[ROW][C]180[/C][C]14.3[/C][C]13.5877551020408[/C][C]0.712244897959184[/C][/ROW]
[ROW][C]181[/C][C]15[/C][C]13.5877551020408[/C][C]1.41224489795918[/C][/ROW]
[ROW][C]182[/C][C]14[/C][C]16.7848484848485[/C][C]-2.78484848484849[/C][/ROW]
[ROW][C]183[/C][C]15.2[/C][C]16.7848484848485[/C][C]-1.58484848484849[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.2875[/C][C]-0.2875[/C][/ROW]
[ROW][C]185[/C][C]24.8[/C][C]18.345[/C][C]6.455[/C][/ROW]
[ROW][C]186[/C][C]22.2[/C][C]18.345[/C][C]3.855[/C][/ROW]
[ROW][C]187[/C][C]14.9[/C][C]16.7848484848485[/C][C]-1.88484848484848[/C][/ROW]
[ROW][C]188[/C][C]19.2[/C][C]16.7848484848485[/C][C]2.41515151515151[/C][/ROW]
[ROW][C]189[/C][C]16[/C][C]15.2875[/C][C]0.7125[/C][/ROW]
[ROW][C]190[/C][C]11.3[/C][C]13.5877551020408[/C][C]-2.28775510204082[/C][/ROW]
[ROW][C]191[/C][C]13.2[/C][C]15.2875[/C][C]-2.0875[/C][/ROW]
[ROW][C]192[/C][C]14.7[/C][C]15.2875[/C][C]-0.5875[/C][/ROW]
[ROW][C]193[/C][C]15.5[/C][C]16.7848484848485[/C][C]-1.28484848484849[/C][/ROW]
[ROW][C]194[/C][C]16.4[/C][C]16.7848484848485[/C][C]-0.384848484848487[/C][/ROW]
[ROW][C]195[/C][C]18.1[/C][C]18.345[/C][C]-0.244999999999997[/C][/ROW]
[ROW][C]196[/C][C]20.1[/C][C]18.345[/C][C]1.755[/C][/ROW]
[ROW][C]197[/C][C]15.8[/C][C]15.2875[/C][C]0.512500000000001[/C][/ROW]
[ROW][C]198[/C][C]15.5[/C][C]15.2875[/C][C]0.2125[/C][/ROW]
[ROW][C]199[/C][C]15[/C][C]15.2875[/C][C]-0.2875[/C][/ROW]
[ROW][C]200[/C][C]15.2[/C][C]15.2875[/C][C]-0.0875000000000004[/C][/ROW]
[ROW][C]201[/C][C]14.4[/C][C]15.2875[/C][C]-0.8875[/C][/ROW]
[ROW][C]202[/C][C]19.2[/C][C]16.7848484848485[/C][C]2.41515151515151[/C][/ROW]
[ROW][C]203[/C][C]19.9[/C][C]18.345[/C][C]1.555[/C][/ROW]
[ROW][C]204[/C][C]13.8[/C][C]16.7848484848485[/C][C]-2.98484848484848[/C][/ROW]
[ROW][C]205[/C][C]15.3[/C][C]16.7848484848485[/C][C]-1.48484848484848[/C][/ROW]
[ROW][C]206[/C][C]15.1[/C][C]15.2875[/C][C]-0.1875[/C][/ROW]
[ROW][C]207[/C][C]15.7[/C][C]15.2875[/C][C]0.4125[/C][/ROW]
[ROW][C]208[/C][C]16.4[/C][C]15.2875[/C][C]1.1125[/C][/ROW]
[ROW][C]209[/C][C]12.6[/C][C]15.2875[/C][C]-2.6875[/C][/ROW]
[ROW][C]210[/C][C]12.9[/C][C]15.2875[/C][C]-2.3875[/C][/ROW]
[ROW][C]211[/C][C]16.4[/C][C]16.7848484848485[/C][C]-0.384848484848487[/C][/ROW]
[ROW][C]212[/C][C]16.1[/C][C]19.6111111111111[/C][C]-3.51111111111111[/C][/ROW]
[ROW][C]213[/C][C]19.4[/C][C]16.7848484848485[/C][C]2.61515151515151[/C][/ROW]
[ROW][C]214[/C][C]17.3[/C][C]16.7848484848485[/C][C]0.515151515151516[/C][/ROW]
[ROW][C]215[/C][C]14.9[/C][C]16.7848484848485[/C][C]-1.88484848484848[/C][/ROW]
[ROW][C]216[/C][C]16.2[/C][C]16.7848484848485[/C][C]-0.584848484848486[/C][/ROW]
[ROW][C]217[/C][C]14.2[/C][C]15.2875[/C][C]-1.0875[/C][/ROW]
[ROW][C]218[/C][C]14.8[/C][C]15.2875[/C][C]-0.487499999999999[/C][/ROW]
[ROW][C]219[/C][C]20.4[/C][C]18.345[/C][C]2.055[/C][/ROW]
[ROW][C]220[/C][C]13.8[/C][C]13.5877551020408[/C][C]0.212244897959184[/C][/ROW]
[ROW][C]221[/C][C]15.8[/C][C]16.045[/C][C]-0.245000000000001[/C][/ROW]
[ROW][C]222[/C][C]17.1[/C][C]18.345[/C][C]-1.245[/C][/ROW]
[ROW][C]223[/C][C]16.6[/C][C]18.345[/C][C]-1.745[/C][/ROW]
[ROW][C]224[/C][C]18.6[/C][C]15.2875[/C][C]3.3125[/C][/ROW]
[ROW][C]225[/C][C]18[/C][C]15.2875[/C][C]2.7125[/C][/ROW]
[ROW][C]226[/C][C]16[/C][C]15.2875[/C][C]0.7125[/C][/ROW]
[ROW][C]227[/C][C]18[/C][C]15.2875[/C][C]2.7125[/C][/ROW]
[ROW][C]228[/C][C]15.3[/C][C]15.2875[/C][C]0.0125000000000011[/C][/ROW]
[ROW][C]229[/C][C]17.6[/C][C]16.7848484848485[/C][C]0.815151515151516[/C][/ROW]
[ROW][C]230[/C][C]14.7[/C][C]16.7848484848485[/C][C]-2.08484848484849[/C][/ROW]
[ROW][C]231[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]232[/C][C]14.5[/C][C]15.2875[/C][C]-0.7875[/C][/ROW]
[ROW][C]233[/C][C]15.7[/C][C]16.7848484848485[/C][C]-1.08484848484849[/C][/ROW]
[ROW][C]234[/C][C]16.4[/C][C]16.045[/C][C]0.354999999999997[/C][/ROW]
[ROW][C]235[/C][C]17[/C][C]18.345[/C][C]-1.345[/C][/ROW]
[ROW][C]236[/C][C]13.9[/C][C]15.2875[/C][C]-1.3875[/C][/ROW]
[ROW][C]237[/C][C]17.3[/C][C]18.345[/C][C]-1.045[/C][/ROW]
[ROW][C]238[/C][C]15.6[/C][C]15.2875[/C][C]0.3125[/C][/ROW]
[ROW][C]239[/C][C]11.6[/C][C]15.2875[/C][C]-3.6875[/C][/ROW]
[ROW][C]240[/C][C]18.6[/C][C]15.2875[/C][C]3.3125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109704&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109704&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	11.5	12.6461538461538	-1.14615384615385
2	11	13.5877551020408	-2.58775510204082
3	10.5	13.5877551020408	-3.08775510204082
4	10	10.7214285714286	-0.721428571428572
5	8.5	10.7214285714286	-2.22142857142857
6	10	10.7214285714286	-0.721428571428572
7	10	10.7214285714286	-0.721428571428572
8	8	12.6461538461538	-4.64615384615385
9	10	10.7214285714286	-0.721428571428572
10	15	15.2875	-0.2875
11	15.5	15.2875	0.2125
12	20.5	19.6111111111111	0.88888888888889
13	17.5	15.2875	2.2125
14	17.5	15.2875	2.2125
15	12.5	13.5877551020408	-1.08775510204082
16	14	12.6461538461538	1.35384615384615
17	15	12.6461538461538	2.35384615384615
18	18.5	12.6461538461538	5.85384615384615
19	14.5	15.2875	-0.7875
20	14	15.2875	-1.2875
21	15.5	16.045	-0.545000000000002
22	15.5	16.045	-0.545000000000002
23	12	12.6461538461538	-0.646153846153846
24	13	13.5877551020408	-0.587755102040816
25	12	10.7214285714286	1.27857142857143
26	12	10.7214285714286	1.27857142857143
27	19	16.7848484848485	2.21515151515151
28	15	16.045	-1.045
29	14	15.2875	-1.2875
30	14	15.2875	-1.2875
31	14.5	15.2875	-0.7875
32	19	16.7848484848485	2.21515151515151
33	19	19.6111111111111	-0.611111111111111
34	20.5	16.7848484848485	3.71515151515151
35	17	15.2875	1.7125
36	16.5	15.2875	1.2125
37	12	12.6461538461538	-0.646153846153846
38	13.5	13.5877551020408	-0.0877551020408163
39	13	13.5877551020408	-0.587755102040816
40	11	10.7214285714286	0.278571428571428
41	13.5	13.5877551020408	-0.0877551020408163
42	12.5	10.7214285714286	1.77857142857143
43	13.5	15.2875	-1.7875
44	14	13.5877551020408	0.412244897959184
45	16	13.5877551020408	2.41224489795918
46	14.5	13.5877551020408	0.912244897959184
47	18	15.2875	2.7125
48	16	15.2875	0.7125
49	14.5	15.2875	-0.7875
50	15	15.2875	-0.2875
51	13	12.6461538461538	0.353846153846154
52	11.5	13.5877551020408	-2.08775510204082
53	14.5	13.5877551020408	0.912244897959184
54	12.5	13.5877551020408	-1.08775510204082
55	12	13.5877551020408	-1.58775510204082
56	13	13.5877551020408	-0.587755102040816
57	11	10.7214285714286	0.278571428571428
58	11	10.7214285714286	0.278571428571428
59	16.5	16.045	0.454999999999998
60	18	16.045	1.955
61	16.5	18.345	-1.845
62	16	16.045	-0.0450000000000017
63	14	13.5877551020408	0.412244897959184
64	12.5	12.6461538461538	-0.146153846153846
65	15	15.2875	-0.2875
66	19.5	16.7848484848485	2.71515151515151
67	16.5	15.2875	1.2125
68	18.5	15.2875	3.2125
69	14	15.2875	-1.2875
70	13	13.5877551020408	-0.587755102040816
71	9.5	10.7214285714286	-1.22142857142857
72	15.5	15.2875	0.2125
73	14	15.2875	-1.2875
74	11	13.5877551020408	-2.58775510204082
75	14	15.2875	-1.2875
76	11	12.6461538461538	-1.64615384615385
77	16.5	16.045	0.454999999999998
78	16	16.045	-0.0450000000000017
79	16.5	15.2875	1.2125
80	21	16.7848484848485	4.21515151515151
81	17	18.15	-1.15
82	18	18.15	-0.149999999999999
83	14	13.5877551020408	0.412244897959184
84	14.5	13.5877551020408	0.912244897959184
85	16	13.5877551020408	2.41224489795918
86	15.5	13.5877551020408	1.91224489795918
87	15.5	16.7848484848485	-1.28484848484849
88	14.5	15.2875	-0.7875
89	19	19.6111111111111	-0.611111111111111
90	14.5	15.2875	-0.7875
91	14	15.2875	-1.2875
92	15	15.2875	-0.2875
93	16	16.7848484848485	-0.784848484848485
94	16	16.045	-0.0450000000000017
95	19.5	18.345	1.155
96	11.5	10.7214285714286	0.778571428571428
97	14	13.5877551020408	0.412244897959184
98	13.5	13.5877551020408	-0.0877551020408163
99	21	18.15	2.85
100	19	18.15	0.850000000000001
101	19	18.15	0.850000000000001
102	13.5	16.045	-2.545
103	12	13.5877551020408	-1.58775510204082
104	17	15.2875	1.7125
105	16	16.045	-0.0450000000000017
106	13.5	15.2875	-1.7875
107	16.5	16.7848484848485	-0.284848484848485
108	14.5	16.045	-1.545
109	15	15.2875	-0.2875
110	17	18.345	-1.345
111	13.5	13.5877551020408	-0.0877551020408163
112	17.5	19.6111111111111	-2.11111111111111
113	16.9	15.2875	1.6125
114	14.9	15.2875	-0.387499999999999
115	15.3	15.2875	0.0125000000000011
116	13	13.5877551020408	-0.587755102040816
117	13.9	13.5877551020408	0.312244897959184
118	12.8	13.5877551020408	-0.787755102040816
119	14.5	16.045	-1.545
120	17.6	18.345	-0.744999999999997
121	22.2	19.6111111111111	2.58888888888889
122	22.1	19.6111111111111	2.48888888888889
123	17.7	18.15	-0.449999999999999
124	16.2	18.15	-1.95
125	17.8	16.045	1.755
126	17	16.7848484848485	0.215151515151515
127	16.4	15.2875	1.1125
128	15.7	16.045	-0.345000000000002
129	13.2	13.5877551020408	-0.387755102040817
130	16.7	13.5877551020408	3.11224489795918
131	12.1	12.6461538461538	-0.546153846153846
132	15	13.5877551020408	1.41224489795918
133	14	13.5877551020408	0.412244897959184
134	14.8	15.2875	-0.487499999999999
135	18.6	19.6111111111111	-1.01111111111111
136	16.8	16.7848484848485	0.0151515151515156
137	12.5	13.5877551020408	-1.08775510204082
138	13.7	13.5877551020408	0.112244897959183
139	16.9	16.045	0.854999999999997
140	17.7	18.15	-0.449999999999999
141	11.1	10.7214285714286	0.378571428571428
142	11.4	12.6461538461538	-1.24615384615385
143	14.5	13.5877551020408	0.912244897959184
144	14.5	15.2875	-0.7875
145	18.2	15.2875	2.9125
146	15.8	15.2875	0.512500000000001
147	15.9	15.2875	0.612500000000001
148	16.4	16.7848484848485	-0.384848484848487
149	14.5	15.2875	-0.7875
150	12.8	15.2875	-2.4875
151	21.5	19.6111111111111	1.88888888888889
152	14.4	16.7848484848485	-2.38484848484848
153	18.6	16.7848484848485	1.81515151515152
154	13.2	13.5877551020408	-0.387755102040817
155	12.8	13.5877551020408	-0.787755102040816
156	18.2	16.045	2.155
157	15.8	18.345	-2.545
158	17.2	18.15	-0.95
159	17.2	18.345	-1.145
160	16.7	18.345	-1.645
161	18.7	18.15	0.550000000000001
162	13.2	13.5877551020408	-0.387755102040817
163	13.4	12.6461538461538	0.753846153846155
164	13.7	13.5877551020408	0.112244897959183
165	16.5	16.7848484848485	-0.284848484848485
166	14.7	15.2875	-0.5875
167	14.5	15.2875	-0.7875
168	17.6	18.345	-0.744999999999997
169	15.9	15.2875	0.612500000000001
170	13.6	13.5877551020408	0.0122448979591834
171	15.8	13.5877551020408	2.21224489795918
172	14.9	16.7848484848485	-1.88484848484848
173	16.6	16.7848484848485	-0.184848484848484
174	18.2	18.345	-0.145
175	17.3	18.345	-1.045
176	16.6	16.045	0.555
177	15.4	13.5877551020408	1.81224489795918
178	13.2	13.5877551020408	-0.387755102040817
179	15.2	13.5877551020408	1.61224489795918
180	14.3	13.5877551020408	0.712244897959184
181	15	13.5877551020408	1.41224489795918
182	14	16.7848484848485	-2.78484848484849
183	15.2	16.7848484848485	-1.58484848484849
184	15	15.2875	-0.2875
185	24.8	18.345	6.455
186	22.2	18.345	3.855
187	14.9	16.7848484848485	-1.88484848484848
188	19.2	16.7848484848485	2.41515151515151
189	16	15.2875	0.7125
190	11.3	13.5877551020408	-2.28775510204082
191	13.2	15.2875	-2.0875
192	14.7	15.2875	-0.5875
193	15.5	16.7848484848485	-1.28484848484849
194	16.4	16.7848484848485	-0.384848484848487
195	18.1	18.345	-0.244999999999997
196	20.1	18.345	1.755
197	15.8	15.2875	0.512500000000001
198	15.5	15.2875	0.2125
199	15	15.2875	-0.2875
200	15.2	15.2875	-0.0875000000000004
201	14.4	15.2875	-0.8875
202	19.2	16.7848484848485	2.41515151515151
203	19.9	18.345	1.555
204	13.8	16.7848484848485	-2.98484848484848
205	15.3	16.7848484848485	-1.48484848484848
206	15.1	15.2875	-0.1875
207	15.7	15.2875	0.4125
208	16.4	15.2875	1.1125
209	12.6	15.2875	-2.6875
210	12.9	15.2875	-2.3875
211	16.4	16.7848484848485	-0.384848484848487
212	16.1	19.6111111111111	-3.51111111111111
213	19.4	16.7848484848485	2.61515151515151
214	17.3	16.7848484848485	0.515151515151516
215	14.9	16.7848484848485	-1.88484848484848
216	16.2	16.7848484848485	-0.584848484848486
217	14.2	15.2875	-1.0875
218	14.8	15.2875	-0.487499999999999
219	20.4	18.345	2.055
220	13.8	13.5877551020408	0.212244897959184
221	15.8	16.045	-0.245000000000001
222	17.1	18.345	-1.245
223	16.6	18.345	-1.745
224	18.6	15.2875	3.3125
225	18	15.2875	2.7125
226	16	15.2875	0.7125
227	18	15.2875	2.7125
228	15.3	15.2875	0.0125000000000011
229	17.6	16.7848484848485	0.815151515151516
230	14.7	16.7848484848485	-2.08484848484849
231	14.5	15.2875	-0.7875
232	14.5	15.2875	-0.7875
233	15.7	16.7848484848485	-1.08484848484849
234	16.4	16.045	0.354999999999997
235	17	18.345	-1.345
236	13.9	15.2875	-1.3875
237	17.3	18.345	-1.045
238	15.6	15.2875	0.3125
239	11.6	15.2875	-3.6875
240	18.6	15.2875	3.3125

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

R code (references can be found in the software module):

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code